
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int]=3     ,				UpperCamelCase__     :					str=32     ,				UpperCamelCase__     :					int=3     ,				UpperCamelCase__     :					Optional[Any]=10     ,				UpperCamelCase__     :					Union[str, Any]=[10, 20, 30, 40]     ,				UpperCamelCase__     :					Tuple=[1, 1, 2, 1]     ,				UpperCamelCase__     :					Union[str, Any]=True     ,				UpperCamelCase__     :					List[Any]=True     ,				UpperCamelCase__     :					Optional[Any]="relu"     ,				UpperCamelCase__     :					Any=3     ,				UpperCamelCase__     :					List[Any]=None     ,				)     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			image_size
    __magic_name__								=			num_channels
    __magic_name__								=			embeddings_size
    __magic_name__								=			hidden_sizes
    __magic_name__								=			depths
    __magic_name__								=			is_training
    __magic_name__								=			use_labels
    __magic_name__								=			hidden_act
    __magic_name__								=			num_labels
    __magic_name__								=			scope
    __magic_name__								=			len(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

    __magic_name__								=			None
    if self.use_labels:
      __magic_name__								=			ids_tensor([self.batch_size]     ,				self.num_labels       )

    __magic_name__								=			self.get_config()
    return config, pixel_values, labels
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    return RegNetConfig(
        num_channels=self.num_channels     ,				embeddings_size=self.embeddings_size     ,				hidden_sizes=self.hidden_sizes     ,				depths=self.depths     ,				hidden_act=self.hidden_act     ,				num_labels=self.num_labels     ,				)
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			TFRegNetModel(config=UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__     ,				training=UpperCamelCase__       )
    # expected last hidden states: B, C, H // 32, W // 32
    self.parent.assertEqual(
        result.last_hidden_state.shape     ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,				)
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Tuple       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.num_labels
    __magic_name__								=			TFRegNetForImageClassification(UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__     ,				labels=UpperCamelCase__     ,				training=UpperCamelCase__       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.num_labels)       )





  def 				_lowercase				(					self     :					int       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.prepare_config_and_inputs()
    __magic_name__	,       __magic_name__	,       __magic_name__								=			config_and_inputs
    __magic_name__								=			{"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_tf
class 			UpperCAmelCase_     (					_A    ,			_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
  a__       			=						(
      {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
      if is_tf_available()
      else {}
  )

  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						False
  def 				_lowercase				(					self     :					str       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			TFRegNetModelTester(self       )
    __magic_name__								=			ConfigTester(self     ,				config_class=UpperCamelCase__     ,				has_text_modality=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Any:







    """simple docstring"""






    return
  @unittest.skip(reason="""RegNet does not use inputs_embeds"""       )
  def 				_lowercase				(					self     :					str       )     ->      Any:







    """simple docstring"""






    pass
  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""       )       ) == 0     ,				reason="""TF does not support backprop for grouped convolutions on CPU."""     ,				)
  @slow
  def 				_lowercase				(					self     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    super().test_keras_fit()
  @unittest.skip(reason="""RegNet does not support input and output embeddings"""       )
  def 				_lowercase				(					self     :					str       )     ->      List[str]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					Any       )     ->      Dict:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      __magic_name__								=			inspect.signature(model.call       )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __magic_name__								=			[*signature.parameters.keys()]

      __magic_name__								=			["""pixel_values"""]
      self.assertListEqual(arg_names[:1]     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    def check_hidden_states_output(UpperCamelCase__     :					str     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Any       ):
      __magic_name__								=			model_class(UpperCamelCase__       )
      __magic_name__								=			model(**self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )     ,				training=UpperCamelCase__       )

      __magic_name__								=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

      __magic_name__								=			self.model_tester.num_stages
      self.assertEqual(len(UpperCamelCase__       )     ,				expected_num_stages + 1       )

      # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
      self.assertListEqual(
          list(hidden_states[0].shape[-2:]       )     ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2]     ,				)

    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()
    __magic_name__								=			["""basic""", """bottleneck"""]
    for model_class in self.all_model_classes:
      for layer_type in layers_type:
        __magic_name__								=			layer_type
        __magic_name__								=			True
        check_hidden_states_output(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __magic_name__								=			True

        check_hidden_states_output(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    def check_equivalence(UpperCamelCase__     :					str     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int={}       ):
      __magic_name__								=			model(UpperCamelCase__     ,				return_dict=UpperCamelCase__     ,				**UpperCamelCase__       )
      __magic_name__								=			model(UpperCamelCase__     ,				return_dict=UpperCamelCase__     ,				**UpperCamelCase__       ).to_tuple()

      def recursive_check(UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int]       ):
        if isinstance(UpperCamelCase__     ,				(List, Tuple)       ):
          for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
            recursive_check(UpperCamelCase__     ,				UpperCamelCase__       )
        elif tuple_object is None:
          return
        else:
          self.assertTrue(
              all(tf.equal(UpperCamelCase__     ,				UpperCamelCase__       )       )     ,				msg=(
                  """Tuple and dict output are not equal. Difference:"""
                  F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}'''
              )     ,				)

        recursive_check(UpperCamelCase__     ,				UpperCamelCase__       )

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )

      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )
      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )
      check_equivalence(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )

      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__     ,				return_labels=UpperCamelCase__       )
      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__     ,				return_labels=UpperCamelCase__       )
      check_equivalence(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       )

      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )
      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__       )
      check_equivalence(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				{"""output_hidden_states""": True}       )

      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__     ,				return_labels=UpperCamelCase__       )
      __magic_name__								=			self._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__     ,				return_labels=UpperCamelCase__       )
      check_equivalence(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				{"""output_hidden_states""": True}       )
  def 				_lowercase				(					self     :					Dict       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )





  @slow
  def 				_lowercase				(					self     :					str       )     ->      str:







    """simple docstring"""






    for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __magic_name__								=			TFRegNetModel.from_pretrained(UpperCamelCase__       )
      self.assertIsNotNone(UpperCamelCase__       )







def a__       (			):






  '''simple docstring'''





  __magic_name__								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image





@require_tf
@require_vision
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  @cached_property
  def 				_lowercase				(					self     :					str       )     ->      int:







    """simple docstring"""






    return (
        AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
        if is_vision_available()
        else None
    )





  @slow
  def 				_lowercase				(					self     :					List[str]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

    __magic_name__								=			self.default_image_processor
    __magic_name__								=			prepare_img()
    __magic_name__								=			image_processor(images=UpperCamelCase__     ,				return_tensors="""tf"""       )

    # forward pass
    __magic_name__								=			model(**UpperCamelCase__     ,				training=UpperCamelCase__       )

    # verify the logits
    __magic_name__								=			tf.TensorShape((1, 1000)       )
    self.assertEqual(outputs.logits.shape     ,				UpperCamelCase__       )

    __magic_name__								=			tf.constant([-0.4180, -1.5051, -3.4836]       )

    tf.debugging.assert_near(outputs.logits[0, :3]     ,				UpperCamelCase__     ,				atol=1E-4       )



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase     :	Dict       			=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						None
  a__       			=						None







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""train"""
  a__       			=						"""dev"""
  a__       			=						"""test"""







class 			UpperCAmelCase_     :


  '''simple docstring'''







  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Union[Split, str]       )     ->      List[InputExample]:







    """simple docstring"""






    raise NotImplementedError
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    raise NotImplementedError





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[InputExample]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Tuple="[CLS]"     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					str="[SEP]"     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					List[str]=0     ,				UpperCamelCase__     :					str=0     ,				UpperCamelCase__     :					Optional[Any]=-100     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Optional[int]=True     ,				)     ->      List[InputFeatures]:







    """simple docstring"""






    __magic_name__								=			{label: i for i, label in enumerate(UpperCamelCase__       )}

    __magic_name__								=			[]
    for ex_index, example in enumerate(UpperCamelCase__       ):
      if ex_index % 1_0000 == 0:
        logger.info("""Writing example %d of %d"""     ,				UpperCamelCase__     ,				len(UpperCamelCase__       )       )

      __magic_name__								=			[]
      __magic_name__								=			[]
      for word, label in zip(example.words     ,				example.labels       ):
        __magic_name__								=			tokenizer.tokenize(UpperCamelCase__       )

        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
        if len(UpperCamelCase__       ) > 0:
          tokens.extend(UpperCamelCase__       )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      __magic_name__								=			tokenizer.num_special_tokens_to_add()
      if len(UpperCamelCase__       ) > max_seq_length - special_tokens_count:
        __magic_name__								=			tokens[: (max_seq_length - special_tokens_count)]
        __magic_name__								=			label_ids[: (max_seq_length - special_tokens_count)]

      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
        # roberta uses an extra separator b/w pairs of sentences
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
      __magic_name__								=			[sequence_a_segment_id] * len(UpperCamelCase__       )

      if cls_token_at_end:
        tokens += [cls_token]
        label_ids += [pad_token_label_id]
        segment_ids += [cls_token_segment_id]
      else:
        __magic_name__								=			[cls_token] + tokens
        __magic_name__								=			[pad_token_label_id] + label_ids
        __magic_name__								=			[cls_token_segment_id] + segment_ids

      __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      __magic_name__								=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__       )

      # Zero-pad up to the sequence length.
      __magic_name__								=			max_seq_length - len(UpperCamelCase__       )
      if pad_on_left:
        __magic_name__								=			([pad_token] * padding_length) + input_ids
        __magic_name__								=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
        __magic_name__								=			([pad_token_segment_id] * padding_length) + segment_ids
        __magic_name__								=			([pad_token_label_id] * padding_length) + label_ids
      else:
        input_ids += [pad_token] * padding_length
        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
        segment_ids += [pad_token_segment_id] * padding_length
        label_ids += [pad_token_label_id] * padding_length

      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length

      if ex_index < 5:
        logger.info("""*** Example ***"""       )
        logger.info("""guid: %s"""     ,				example.guid       )
        logger.info("""tokens: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in tokens]       )       )
        logger.info("""input_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_ids]       )       )
        logger.info("""input_mask: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_mask]       )       )
        logger.info("""segment_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in segment_ids]       )       )
        logger.info("""label_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in label_ids]       )       )

      if "token_type_ids" not in tokenizer.model_input_names:
        __magic_name__								=			None

      features.append(
          InputFeatures(
              input_ids=UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				token_type_ids=UpperCamelCase__     ,				label_ids=UpperCamelCase__       )       )
    return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset




    class 			UpperCAmelCase_     (					_A	):


      '''simple docstring'''







      a__       			=						42
      a__       			=						nn.CrossEntropyLoss().ignore_index
      def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      str:







        """simple docstring"""






        __magic_name__								=			os.path.join(
            UpperCamelCase__     ,				"""cached_{}_{}_{}""".format(mode.value     ,				tokenizer.__class__.__name__     ,				str(UpperCamelCase__       )       )     ,				)

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __magic_name__								=			cached_features_file + """.lock"""
        with FileLock(UpperCamelCase__       ):
          if os.path.exists(UpperCamelCase__       ) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''       )
            __magic_name__								=			torch.load(UpperCamelCase__       )
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''       )
            __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            __magic_name__								=			token_classification_task.convert_examples_to_features(
                UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)
            logger.info(F'''Saving features into cached file {cached_features_file}'''       )
            torch.save(self.features     ,				UpperCamelCase__       )
      def __len__(					self     :					List[str]       )     ->      Any:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]


if is_tf_available():
    import tensorflow as tf




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						42
      a__       			=						-1_00
      def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      List[Any]:







        """simple docstring"""






        __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
        # TODO clean up all this to leverage built-in features of tokenizers
        __magic_name__								=			token_classification_task.convert_examples_to_features(
            UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)

        def gen():
          for ex in self.features:
            if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
            else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

        if "token_type_ids" not in tokenizer.model_input_names:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,				(
                  {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )     ,				)
        else:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,				(
                  {
                      """input_ids""": tf.TensorShape([None]       ),
                      """attention_mask""": tf.TensorShape([None]       ),
                      """token_type_ids""": tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )     ,				)
      def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







        """simple docstring"""






        __magic_name__								=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

        return self.dataset
      def __len__(					self     :					Tuple       )     ->      Tuple:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]

